You are listening to the HumAIn Podcast. HumAIn is your first look at the startups and industry titans that are leading and disrupting artificial intelligence, data science, future of work and developer education. I am your host, David Yakobovitch, and you are listening to HumAIn. If you like this episode, remember to subscribe and leave a review. Now onto the show.
Welcome back to the HumAIn Podcast. Today, I’m bringing a special guest from the West Coast. Today’s guest is Jerry Ting¹ and he is the founder and CEO of Evisort² which is an AI powered contract analytics platform.
I first got to know Jerry through one of our mutual colleagues, Pat Yang, with Amity Ventures, who happens to be one of their lead investors as their startup has been scaling up. It’s a very exciting space and a legal tech. And what is that whole industry is evolving very quickly. So I’d love to learn more about it today. And Jerry, thanks so much for being with us.
David. It’s my pleasure.
So starting off with legal tech. I’m a New Yorker. When we think of law firms and legal in New York, everyone thinks of the big firms, the super firms, the super lawyers, but the legal space is evolving and changing very quickly. So tell us about yourself, legal tech and why that’s the venture pathway that you’re exploring and growing with Evisort?
I love the New York background because I actually had spent some time in a law firm in New York before starting Evisort. I’m a lawyer by background originally from the Bay area.
So grew up loving technology, being a fan of. And I remember back in high school, we would get together and watch Apple keynotes with Steve Jobs. And those are things that my friends and I did growing up. And so we’re always fans of technology, but I always had a passion for law. And so I went to Harvard for law school and then worked also in New York.
And so, law is one of those things where it’s a super fascinating industry in the sense that it’s one of the last ones to typically adopt technology. And so, one of the things that surprised me as a millenial is when I got the law practice, the type of tools that we’re using, the type of work that people were doing, a lot of legacy tools that are focused on solving something very specific. Like one, one example was billing. As you might know, #lawyers bill every six minutes. So don’t talk too long on the phone, we actually have billing software that tracks how long, how many, six minute intervals we’re talking.
And so that was the type of tools that we were using, but nothing with automation, artificial intelligence, business intelligence. And so, if you flip to your insight life, when you’re at home, we use Dropbox, we use Google drive, we collaborate on Apple cloud. But then you go into the law firm environment or the legal environment, and then we stepped back 10 to 15 years in technology.
And so the question is why is it that right there in the middle of Manhattan and charging people, clients $600 to $800 an hour as a young associate, we’re using technology that really has been available for the last 10 to 15 years. And so, #legaltech is one of those terms that emerged recently, but it’s a new wave of technology that addresses the question of how do we make lawyers more efficient.
When they think of the legal industry, it’s a lot of what you’re saying, Jerry, that one of my favorite shows from a few years ago was the Good Wife. And that’s classic story of law firms, Alicia Florrick, and most of it’s all about the law battles and the drama that we know on TV shows, but occasionally even the episodes would dive into tech and you would almost see it as a parody and the comedy of how in the mold the technology is when they ran into issues and, as a consumer, you would think, that’s just what we’ll see on TV, but perhaps that’s actually what’s going on at #lawfirms. Because of that, 10 to 15 year lag in technology now is a ripe opportunity for disruption in the legal space.
Absolutely. What I learned about law practice as I was apprenticing to become a lawyer, the stories I heard from lawyers who are just ahead of me a few years it shocked me. So I’ll share two examples.
One is a deal document review, which is if you’re going through a merger acquisition, a company is buying another company. As recent as 10 years ago, you would get banker boxes, thousands and thousands of pages rent out a conference room in a hotel and bring in an army of associates and manually go through a one by one with sticky notes.
This was happening even during, 2008, 2009 during the financial recession. It’s very recent. Taking that and, just moving that to the #cloud has created multiple billion dollar businesses just taking the fallen cabinet and creating deal rooms.
Another example is, gathering signatures. This still happens today, but it’s the job of a yellow associate to go and actually gather your signature pages, print them out, go to customers and clients, get the contract signed, and then basically put them all together in a PDF and they’ll charge customers and clients. Hundreds of hours to basically do those two processes e-signature has been around for 10 years or 15 years.
So, when I saw what was actually happening in law, I realized, wow, there’s a really big market opportunity here to both modernize and also look forward, bringing in automation and artificial intelligence to really help industry that provides a lot of value, but hasn’t adopted technology in the way that I would say, financial, counterparts have.
So looking at the market now, and we’re in 2020, what part of the market did you decide was where Evisort and your team could best help advance legal tech to modern day?
So the thing about lawyers is that there are two types of lawyers and so most legal tech companies could be split between these two types. There are those who litigate, who actually are suing and trying to resolve a matter. Then there’s corporate lawyers. So my background is I am a corporate lawyer. I’m not a litigator, although I have litigators who now work for me at Evisort, who’ve crossed the bridge.
And it’s so interesting. The rise of e-discovery, several companies there have raised hundreds of millions of dollars and e-discovery is something that has been invested a lot into #predictiveanalytics over the last 10 to 15 years. What you see as this very interesting split inside of law firms where a partner that does litigation knows how to do predictive analytics, predictive coding in house relationships with vendors, you go down one floor on the same New York high rise. They’re a corporate partner and they’re using Excel to track the afloat.
It’s even in the same law firm, just by going from litigation to corporate, there’s a big drop-off in tech adoption. And so for Evisort knowing that we focus on the corporate side, because we also realize that the function of legal tech. Goes beyond just the legal function of lawyers, supporting financial bankers to supporting procurement clients, they’re supporting sales.
And for us, we focus actually on working more in house than we do in law firms, because that’s where the economic model aligns better. If you think about law firms, and this is admittedly changing, but historically law firms bill on an hourly basis. And so, if you bring in tools that save 80% of time, that might not necessarily be all good for a #lawfirm for an in-house counsel, for a lawyer at Microsoft, for a lawyer at, and name any big firm, they’re driven by traditional business KPIs. So being more efficient, being able to help close deals quicker, removing roadblocks for sales and procurement. These are good things for in-house counsel. And so we focus on in-house corporate counsel.
As you mentioned, it’s very different from e-discovery. We have the big companies that have raised a lot of the capital, like Epic and Disco and others, but there’s this whole in-house counsel arena that everyone knows unique counsel, whether you’re a startup, whether you’re a fortune 500. Everyone has in-house counsel, but as you mentioned, it’s a lot of money, it’s a lot of time and there has to be a way to make it more efficient.
Now, before we dive deeper into what you’re doing right now, of course in the legal tech space, anyone who’s a lawyer or who’s in this space has noticed in the news that, one of the major ventures in this space has had a lot of shifts and changes lately. So from what you’re seeing in the legal tech arena with companies like Atrium, founded by the former founder of Twitch. They’ve run into some troubles lately with hiring and pivoting. And, what do you think of the arena? It sounds like there’s a lot of change happening right now in legal tech.
Atrium is a great company and I know some of the operators at Atrium. Wherever you go through the amount of change that we’re seeing the legal industry go through, it’s not easy. It’s actually easier to change technology. Then it is to change people’s minds. And what Atrium has done well, is actually become a law firm, a source themselves, and provide services to their clients, but in an alternative model.
And so whenever you do that, the incumbents are going to be a little surprised, a little curious. And so for Atrium, they are pushing the envelope on what it means to provide legal services. And so, some hiccups are expected, but they’re focused on #technology, the background of the founders, the overall mission of, we think we can provide legal services, whether it’s tech enabled or with alternative billing models.
And those are truisms. Atrium is going to figure it out and I hope that they do, because I do think that there is a large opportunity for disruption in the law firm space. I hope to see it happen in my lifetime.
You’re right. There will definitely be changes in your lifetime, yourself and there that you’re a venture recently announced the $15 million series A funded by M12 and Vertex. So you’re going through a lot of growth. You’re seeing some product adoption, some traction, for sure. What are some of the good use cases that you can disclose where you’re seeing an Evisort put to work today?
M12 is Microsoft’s central arm, Microsoft is an investor. And the Evisort part of why that’s exciting to me is almost 80% of our customers use SharePoint or Microsoft teams to store contracts in one way or another. And so if you think about a SharePoint is a really good repository, but it’s sometimes difficult for clients to understand, I want to go across 10,000 contracts.
Understand what are the key legal terms? When do they expire? When do my vendor contracts expire? How much money do I owe them? Am I actually paying them and building them correctly?
For us, one of the main use cases is taking data that already exists in the cloud and activating it using machine learning and AI. And so, the key use cases are I’ll bring that to three. One is for, helping accelerate deals, helping accelerate how quickly a sales team can close contracts. Because we can provide a layer of automation to review contracts for proof.
The other one is vendor management. And so being able to see across a billion dollar supply chain. Where in North America are my software license agreements. How much am I paying? When do I have to cancel which ones automatically renew, put that all in a calendar format and visualize that. And the third one is one that encompasses both of the previous, which is bringing data to lights.
And that’s a centralized enterprise repository where regardless of where your contracts are stored, sales contracts could be in Salesforce. Employment contracts could be in Workday. Vendor contracts could be in SAP Ariba, but one centralized place where management can go and find and run a report and gather insights about their contracts across the entire #enterprise.
It’s amazing to see how you’ve, in such a short time period, already discovered your product market fit and working with startups. That’s one of the biggest challenges seeing where do customers have their biggest pain points to be solved, and between accelerating deals, vendor management, access, and getting insights in the data, it sounds like you have three very robust pathways to growth.
And that’s where you are now, but I’m sure as the CEO, you have some vision for the direction of Evisort and where that looks like over the next one to two years. Could you paint us a little bit of that picture?
Absolutely. So our AI technology does a couple of things. We can take a scan of the contract that we’ve never seen before. How could we convert it to a Word file and then pull out over 50 different data points, including who the contract is with, when does it expire and what are the key legal terms? We can do that all today. Going forward, we have a lot of clients who then have asked us, can we go further? Can we actually read a contract for them using #artificialintelligence and tell them, where should I negotiate this contract?.
Where is the from a content analysis perspective based on benchmark data, how do we optimize this contract? And that’s the next level of intelligence that gets me really excited. That’s the type of thing that traditionally has been based on human experience, but we have access to over 5 million contracts that we own proprietarily.
And so from a record, a truth perspective from a what’s market perspective and what’s fair perspective in ever so has an opinion. And so going forward, increasingly we’re going to provide more content specific recommendations. For things like sales contracts, is this a pricing term average, or can you recognize more price based on the deal economics, for vendor contracts?
Can we enjoy pricing, consolidation and pricing flexibility by aggregating, purchasing across the enterprise leases. Can we actually go across leases for the entire company and recognize, when do you actually have to renew those leases?. Because, some of these leases can get hairy. And so as our #machinelearning gets more robust, we’re building verticalized use cases that to be honest, when I started Evisort four years ago, never thought it was possible, but today we’re doing some of that already.
From what you’ve just shared, two of the biggest areas that are necessary for successful AI implementation and deployment are both research and the data. So first I wanted to dive into research. What is your team doing today to get better AI, machine learning as specifically, it sounds like you have all these documents and contracts, so there might be things like NLP and OCR and #computervision. Maybe some of this is being used. So what is your team doing around research?
All of the above. And so, let me walk through the steps we take to build the model. So, how Evisort was built. I was practicing as a volunteer, a student lawyer out of Harvard law school. And my client was getting his PhD at the time at MIT. So I was working as his lawyer and I was using some pretty legacy technology.
And so I asked them, I was like, look, is there a way traditionally, this has been done by humans. We look at contracts. We look for XYZ clauses. We look for XYZ data points, it is impossible to use technology to do that automatically, pull it out and classify it and then maybe put it into a report so the human can read it.
And that to this day is still how we do our work. We understand what the customers need, what are the five data points that we’ll really get this customer? And then, we go to our research team and we have already models that we built that we’ll test with. And most of them are deep learning models, a lot of research being done on natural language processing on computer vision.
And so, what we do is we test it on the existing models that we have. And then if the accuracy is not where we need it to be, we then start to tune that model and then add additional features. We test, we get training data. I have over 20 legal analysts working for me every day, just creating training data. And so we have a large training set that we can lean on. And so at that point, it becomes a matter of a #dataset, the type of models that you use and then QA. And so that’s a three-step process that we can get from an idea I want to pull out expiration dates to here’s a model that’s over 95% accurate.
I have a hunch in the suspicion, in the data science space, having been an instructor and working on different projects in data science and AI, I’ve been coming up with my own thesis on the industry and I’ll be having a publication. That’s going to be talking about this this year, but the workflow typically for any venture starts out with the data aggregation and the data collection, and then moves to some sort of data labeling, data structuring then moves to some sort of feature enrichment or data expansion.
Then into that machine learning model algorithms and then deployments slash QA, ethics, privacy, and whatnot. Of those five pillars, assuming that’s the truth that I’m coming from. The first three are essential. I actually think too many companies are focusing on the final two. Let me get a better algorithm that will optimize. Let me figure out the privacy, but as you just described, you have 20 legal analysts.
And it sounds like a majority of the time is not necessarily creating new algorithms, but between craving and gathering data and QA and modifying these features, that’s a lot of the focus. So my question to you from the initial two pillars I shared before that, it’s all about research and data to hear from your side. Why is data, do you think so important for Evisort?
Data is important for us because without our models it wouldn’t work the way that our clients want it to work. It’s easy to get on a whiteboard. And in theory, design an algorithm that should work. And then the first customer you get to that gives you an out of sample contract. That looks nothing like your training set. That’s when everything falls apart. We’ve learned that with our first couple counts, to be honest.
And so we went back to the drawing board and we said, it’s easy to get to 80% accuracy. On a whole. And then at that point, every basis point needs to be earned, whether it’s, and we found that when you get to about 85% accuracy, you might have to go to completely different algorithms. So everything you did before then might have to get thrown out the door.
But the thing that holds true across all of the modeling, all the research is if you have a diversified dataset and you find a model that fits the data to accurately die is your best bet. And that’s how you control for the surprises where you go into a customer environment and it does something different than what it did in the lab. You should be training on real production, like data.
And so that’s why we’ve invested a significant portion of our R and D budget and building out a proprietary dataset that now spans hundreds of thousands of labeled data points. And the modeling then follows that. But without a large enough data, you might be building a model for the wrong subset of data. It might be under a fitted model.
Another thing that I’ll say is from a scalability perspective, we’re creating training data that customers may not have ordered yet, but we know that as a phase two and a transformation project they may need. And so we’re actually creating training data ahead of our product roadmap so that when the customer orders come. We can activate very quickly instead of waiting for a customer order to come, then trying to get training data, then modelling now you are several weeks behind.
And so, speaking of product roadmap and product releases, there’s always opportunity for new features to be added in all software. And, I’ve been learning a lot about product management just recently in fact, I was in New York at a panel at Bloomberg with Cornell Tech where the CEO via their ride hailing and dynamic transportation company. Was giving a talk, and they were sharing about features.
They’re always adding new features, seeing what their customers can do better and learning from the feedback. So, all products always are having new releases. Some are incremental improvements, some are large improvements. So love to hear any new product releases that your team is working on.
So we just announced a much more robust platform that surrounds our AI capabilities. So when about contracting, there are things you do before you sign a contract, and then there’s other things you do after you sign a contract.
Historically contract management and AI vendors have focused on the things to do, after you sign a contract, we just announced recently a full collaboration platform from generating a contract, to negotiating it, to getting it approved all assisted by AI. That’s now available to all of our clients. And so we are the first company to go end-to-end from the creation of a contract all the way through renewal, all AI assistants all in one platform.
At the end of 2019, that phrase you just used end-to-end started becoming real, Jerry, I saw that at the Strata O’Reilly conference where different companies were saying end-to-end machine learning end-to-end AI, but we haven’t seen end-to-end platforms for different industries yet like yours in legal tech. So, it’s really fascinating to see what your building is end-to-end. And perhaps this is a theme we’re going to see in many verticals in 2020 is the emergence of end-to-end platforms.
There’s a big difference between SAS companies and AI companies. And a lot of people confuse the two. AI companies, the comments I made earlier about research, creating training data, having 20 legal analysts, SAS companies don’t do that. That’s not their DNA. And so traditionally SAS companies are building #software that can be deployed in the cloud and you can sign up with a credit card and you have a platform there.
Our idea is why don’t we combine the two? Why don’t we combine deep AI analytics that were traditionally meant for large enterprises working with consultants. Democratize the AI that’s easily digestible and verticalized for business function and then wrap it in a SAS platform so that anybody can use it.
And so, AI companies mature, they’re going to build more end-to-end SAS platforms. And, it is going to be hard for the SAS platforms to build the AI capabilities. And that over time to emerge into end-to-end SAS and AI platforms.
That is a big phrase end-to-end SAS and AI platforms. And you’re right, because we’re seeing everything merge, we traditionally have. The data scientist. And then we have the software engineer when it came to tech employees at startups and just in the past few years now there’s the machine learning engineer. There’s the AI specialist.
There’s the #datascientist who’s expected to know Java. So this, all this, hybrids roles. And that means we’re going to see hybrids of companies that are adjusting their business models to be software as a service focused. While being end-to-end this platform, which is unique, but also shows a maturity of the market.
I love all that you’ve shared. And I wanted to now segue the conversation to a little bit on East Coast versus West Coast. West Coast, not the West Coast. Who knows, as you mentioned earlier, in our episode, you’ve done a lot of work in New York and Boston on the East Coast, and now you’re based on the West Coast and you’ve also studied there in your ventures been there on both sides, but what’s your take on it. Why are you guys now on, mostly on the West Coast?
It’s an age old question. And I apologize to the friends on both coasts, because my answer is going to be imperfect for both. When we started our company in Boston, it was one of the best cities to start a company, because in Silicon Valley, when you’re starting companies, there’s a lot of distractions.
There are incubators, there are angel investors who write you check. Sometimes companies are not ready for that when they’re still in the idea phase. So when we’re in the idea phase, we were actually in school at Harvard getting our law degrees. And so we were surrounded by some of the most forward thinking, intelligent academic research minds in the world.
So my co-founder was at MIT and I’m in a PhD program at a research lab. And so we were actually purely doing research and that’s how for two years, our product was in light years ahead of our competitors in the industry. And that’s hard to see in the bay area sometimes because of the commercial pressures that come with being venture backed.
But I do think that the bay area is world-class for scaling companies. The leaders and go to market and marketing and sales and customer success, product management, the go-to-market team in the environment that we have in the bay area is hard to compete with, including New York.
But New York is actually one of the main bases for customers. And so for me, I’m always on planes because I’m trying to get the best of all three regions, deep research out of universities in Boston, meeting with clients in New York, and then also running my office here in California. I don’t think one is better than the other.
There’s just different. For depending on what kind of company you are, if you’re a consumer tech company, the bay area is quite good for that. If you’re building enterprise software applications, there’s a lot of clients in New York. And so it’s trying to balance, and in the foreseeable future, we should, we would open an office in New York to increase our presence there because we have a lot of clients there.
That couldn’t have been better said, being a bi-coastal or between both coasts makes a lot of sense, but like you said, there’s nothing like Silicon Valley for scaling. And perhaps there’s nothing like New York city for landing new business. So maybe both worlds tie very well together, as well recently you announced that you have become a Forbes 30 under 30 member.
I’ve heard about this before. It’s one of my favorites for publications to read and I have some friends who’ve in different industries, but come Forbes 30 under 30 with hospitality and travel. But, I actually don’t know much about it. So what has it meant to you to be a Forbes 30 under 30?. And how has that helped you connect with other founders of startups in the space?
It’s a big honor. I never thought I would get it. And so, one of the things that has done for me is the first thing I’ll say. It wasn’t just me that got it. My two co-founders also got it. So three people out of Evisort got 30 under 30 the same year. So our class photo has the three of us in our office, which are that I’m actually very proud of because it’s not just me as a founder building company, my co-founders are here every step of the way. What they have done is, it’s given us some credibility and some recognition for the work that we’re doing.
It’s hard in the early stages, in the early innings of building a startup and to differentiate the people who actually want to build companies are just the people who maybe read some self-help books and then are more exploring the idea of printing a company. And so for us, from day one, we were never doing this as a hobby, we always believed in the vision and our ability to execute and then being named to the Forbes list was a validation for the efforts that we had so far.
And then shortly after Microsoft and Vertex and other VCs invested $15 million. And so now we’re there and there’s financial recognition there as well, but the 30 under 30 was a way for us to go out to our colleagues, peers and say, take a chance at Evisort and join us. We’re here working on something cool, something meaningful and something impactful. Why not do it together?
And that’s very noble and also what’s very meaningful and impactful is the future of work and enhancing jobs. And, one common theme that I feel for certain I’ve taken throughout our whole conversation is that what you’re looking to do is enhancing jobs not replacing jobs.
And I go back to the brief interlude I shared earlier on the event I attended with Scarlet Fu and Daniel Ramote from Via, at Bloomberg in New York. And it was even though it’s a different industry, I take so much parallels. Between your companies, because Scarlet from Bloomberg television asked Daniel, she said, how are you any different than Lyft and Uber?
Are you just, doing contractors and gig economy and replacing jobs. And Daniel said a very surprising fact. He said a couple of years ago via pivoted. We’re no longer just a ride hailing company. We’re a dynamic transportation company. So when you have school buses in cities and other areas like Austin, Texas, that may have not had a bus network. They’re powering that. So that those who are doing manual transport and transport to the 1990s are now brought into 2020.
And so I can’t help, but see a similar parallel. For what your venture is doing with lawyers and law firms to bring them into 2020. And so, with those remarks, I wanted to debate a little bit with you about both, not just what you’re doing, but do you think AI is going to be that arch nemesis, that’s gonna more replace jobs or enhance jobs? Like, what you guys are working on today?
It’s the example that I give here is the difference between automation and augmentation, you’re always going to need a lawyer to review a #contract, but the example that I’ll give is I currently, I just bought a Tesla and it’s been a lot of fun.
It’s actually much faster than I expected because the battery just turns on when you press the gas pedal, which is not a gas pedal, it’s a battery paddle that’s pretty quick and acceleration, but more importantly, it can drive by itself, quote unquote, drive by itself with the autopilot feature. So I’m going down the freeway and I have the autopilot on and it’s driving. It can break it, it could switch lanes.
But what I don’t do is I don’t fall asleep because I’m not trying to die. So what I do is I’m awake at the wheel, but what it does, it frees me up from the stress, the constant monitoring of ‘am I in my lane’, it removes all the parts about driving that I didn’t like. But I do like going places. I do like seeing things. And, that’s what’s happening a lot with verticalized AI applications right now is it’s removing some of the tedious parts of a person’s job, but it’s actually making that person more effective in doing what they were supposed to do in the first place.
And for me, I went to law school because I actually wanted to be a lawyer. I wanted to work with entrepreneurs. I wanted to be a startup lawyer. And help people who are visionaries actually build their companies. If I felt like I was actually able to do that, I would have never started to have Evisort. But what I realized instead was that that’s not what my job was going to be for the first five to 10 years is going to be a lot of manual tasks. A lot of things that can be better done by computers, but the original motivation of being a lawyer still exists in me. And which is why I mentor some #startupfounders now.
And so a long way of saying, I don’t think AI is going to replace people’s jobs. It’s actually going to replace the points that people didn’t want to do in the first place, so they can spend more of their time doing the strategic work.
So in essence, one of the takeaways that I see is, it sounds like you think that AI is going to move society. Faster to its potential to where it could be. We don’t know where that is. We can predict and create all the trend reports we want, but to get to what life could be like in 2100. Why don’t we get there now?
I know in the past few weeks, the Brookings Institute famous for a lot of their tender reports, came out with an article where they said whichever country has figured out and leads in AI by 2030, they will control the planet by 2100. I saw those stronger marks and I said, Oh my gosh, we’re in it. It’s a race. But I don’t think the pressure has to be there about thinking the planets on the line. It’s about our society and our culture. It’s about being human. It’s about working together.
And, as you’ve mentioned, and I’ve seen all these parallels throughout our conversation today, it’s that the law space just has not caught up quite frankly, in the past few decades. And now is that time and I can only imagine you spend five, 10 years doing all this manual work at a law firm. Like what we would see in the Good Wife with Alicia Florrik and their associates. And it’s not something that we have to stand by to see you anymore.
And that’s probably one of the great reasons you’re recognized with Forbes 30 under 30. And your team is now changing the legal tech space in the United States and potentially even more than that. So thank you for all the great work. And I’m looking forward to following everything Jerry, that you and your #Evisort team are doing, especially, once you guys opened the New York office, I say always come back to New York. That’s where lawyers are made.
No, absolutely. I’m in New York almost once every three to four weeks. And so. Thanks so much, David, for the podcast and look forward to sharing the word about AI and legal tech with your audience.
Excellent. Well, thanks so much for being on the HumAIn Podcast and let’s keep everything as a human in the loop process. Thanks so much Jerry.
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